{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kats 101 - Basics\n",
    "\n",
    "Kats (**K**its to **A**nalyze **T**ime **S**eries) is a light-weight, easy-to-use, extenable, and generalizable framework to perform time series analysis in Python.  Time series analysis is an essential component of data science and engineering work.  Kats aims to provide a one-stop shop for techniques for univariate and multivariate time series including:\n",
    "\n",
    "1. Forecasting  \n",
    "2. Anomaly and Change Point Detection  \n",
    "3. Feature Extraction \n",
    "\n",
    "\n",
    "and after introducing the basic Kats data structure, this Kats 101 notebook provides a basic introduction to each of these time series techniques in Kats.  The complete table of contents for Kats 101 is as follows: \n",
    "\n",
    "1. Kats Basics          \n",
    "    1.1 Initiate `TimeSeriesData` Object         \n",
    "    1.2 `TimeSeriesData` built-in operations         \n",
    "2. Forecasting with Kats         \n",
    "    2.1 An example with Prophet model         \n",
    "    2.2 An example with Theta model         \n",
    "3. Detection with Kats         \n",
    "    3.1 What are the algorithms?         \n",
    "    3.2 An example with outlier detection method         \n",
    "    3.3 An example with CUSUM algorithm         \n",
    "4. Feature extraction with Kats         \n",
    "5. Summary         "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** We provide two types of tutorial notebooks\n",
    "- **Kats 101**, basic data structure and functionalities in Kats (this tutorial)  \n",
    "- **Kats 20x**, advanced topics, including advanced forecasting techniques, advanced detection algorithms, `TsFeatures`, meta-learning, etc. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Kats Basics\n",
    "`TimeSeriesData` is the basic data structure in Kats to represented univariate and multivariate time series.  There are two ways to initiate it, henceforth referred to as \"Method 1\" and \"Method 2\":\n",
    "\n",
    "1) `TimeSeriesData(df)`, where `df` is a `pd.DataFrame` object with a \"time\" column and any number of value columns.\n",
    "\n",
    "2) `TimeSeriesData(time, value)`, where `time` is either a `pd.Series` or `pd.DatetimeIndex` object and `value` is either a `pd.Series` (for univariate) or a `pd.DataFrame` (for multivariate)\n",
    "\n",
    "## 1.1 Initiate `TimeSeriesData` object\n",
    "We will use the `air_passenger` and `multi_ts` datasets to demonstrate how to create a `TimeSeriesData` object for univariate and multivariate time series, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "# For Google Colab:\n",
    "!pip install kats\n",
    "!wget https://raw.githubusercontent.com/facebookresearch/Kats/main/kats/data/air_passengers.csv\n",
    "!wget https://raw.githubusercontent.com/facebookresearch/Kats/main/kats/data/multi_ts.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import datetime, timedelta\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "sys.path.append(\"../\")\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from kats.consts import TimeSeriesData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949-01-01</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949-02-01</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949-03-01</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949-04-01</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949-05-01</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         time  value\n",
       "0  1949-01-01    112\n",
       "1  1949-02-01    118\n",
       "2  1949-03-01    132\n",
       "3  1949-04-01    129\n",
       "4  1949-05-01    121"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "try: # If running on Jupyter\n",
    "    air_passengers_df = pd.read_csv(\"../kats/data/air_passengers.csv\")\n",
    "except FileNotFoundError: # If running on colab\n",
    "    air_passengers_df = pd.read_csv(\"air_passengers.csv\")\n",
    "\n",
    "\"\"\"\n",
    "Note: If the column holding the time values is not called \"time\", you will want to specify \n",
    "the name of this column using the time_col_name parameter in the TimeSeriesData constructor.\n",
    "\"\"\"\n",
    "air_passengers_df.columns = [\"time\", \"value\"]\n",
    "air_passengers_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>v1</th>\n",
       "      <th>v2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-03-12</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>53.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-03-13</td>\n",
       "      <td>0.000</td>\n",
       "      <td>53.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-03-14</td>\n",
       "      <td>0.178</td>\n",
       "      <td>53.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-03-15</td>\n",
       "      <td>0.339</td>\n",
       "      <td>53.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-03-16</td>\n",
       "      <td>0.373</td>\n",
       "      <td>53.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         time     v1    v2\n",
       "0  2017-03-12 -0.109  53.8\n",
       "1  2017-03-13  0.000  53.6\n",
       "2  2017-03-14  0.178  53.5\n",
       "3  2017-03-15  0.339  53.5\n",
       "4  2017-03-16  0.373  53.4"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "try: # If running on Jupyter\n",
    "    multi_ts_df = pd.read_csv(\"../kats/data/multi_ts.csv\", index_col=0)\n",
    "except FileNotFoundError: # If running on colab\n",
    "    multi_ts_df = pd.read_csv(\"multi_ts.csv\", index_col=0)\n",
    "multi_ts_df.columns = [\"time\", \"v1\", \"v2\"]\n",
    "multi_ts_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we construct `TimeSeriesData` objects for each time series using Method 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "air_passengers_ts = TimeSeriesData(air_passengers_df)\n",
    "multi_ts = TimeSeriesData(multi_ts_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'kats.consts.TimeSeriesData'>\n",
      "<class 'kats.consts.TimeSeriesData'>\n"
     ]
    }
   ],
   "source": [
    "# check that the type of the data is a \"TimeSeriesData\" object for both cases\n",
    "print(type(air_passengers_ts))\n",
    "print(type(multi_ts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "<class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "# For the air_passengers TimeSeriesData, check that both time and value are pd.Series\n",
    "print(type(air_passengers_ts.time))\n",
    "print(type(air_passengers_ts.value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "# For the multi_ts TimeSeriesData, time is a pd.Series and value is a pd.DataFrame\n",
    "print(type(multi_ts.time))\n",
    "print(type(multi_ts.value))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we show how to construct the same `TimeSeriesData` objects as before using Method 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "air_passengers_ts_from_series = TimeSeriesData(time=air_passengers_df.time, value=air_passengers_df.value)\n",
    "multi_ts_from_series = TimeSeriesData(time=multi_ts_df.time, value=multi_ts_df[['v1', 'v2']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`TimeSeriesData` can accomodate time expressed as a variety of different types, including \n",
    "- standard `datetime`, \n",
    "- `pandas.Timestamp`,\n",
    "- a `str` (if in a non-standard format or if efficiency is important, use the optional `date_format` argument),\n",
    "- `int` (i.e. unix time).\n",
    "\n",
    "Here is an example of how to construct a `TimeSeriesData` object when time is provided in unix time format.\n",
    "\n",
    "\n",
    "Here's an example where the time is auto-interpreted from a unix time format. Using this format just requires a couple optional parameters in the `TimeSeriesData` constructor:\n",
    "- `use_unix_time = True`\n",
    "- `unix_time_units=\"s\"` (the default is `\"ns\"`, indicating nanoseconds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     -662662800.0\n",
       "1     -659980800.0\n",
       "2     -657561600.0\n",
       "3     -654883200.0\n",
       "4     -652291200.0\n",
       "          ...     \n",
       "139   -297190800.0\n",
       "140   -294512400.0\n",
       "141   -291916800.0\n",
       "142   -289238400.0\n",
       "143   -286646400.0\n",
       "Name: time, Length: 144, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dateutil import parser\n",
    "from datetime import datetime\n",
    "\n",
    "# Convert time from air_passengers data to unix time\n",
    "air_passengers_ts_unixtime = air_passengers_df.time.apply(\n",
    "    lambda x: datetime.timestamp(parser.parse(x))\n",
    ")\n",
    "\n",
    "air_passengers_ts_unixtime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949-01-01 07:00:00</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949-02-01 08:00:00</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949-03-01 08:00:00</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949-04-01 08:00:00</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949-05-01 08:00:00</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>1960-08-01 07:00:00</td>\n",
       "      <td>606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>1960-09-01 07:00:00</td>\n",
       "      <td>508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1960-10-01 08:00:00</td>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>1960-11-01 08:00:00</td>\n",
       "      <td>390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>1960-12-01 08:00:00</td>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   time  value\n",
       "0   1949-01-01 07:00:00    112\n",
       "1   1949-02-01 08:00:00    118\n",
       "2   1949-03-01 08:00:00    132\n",
       "3   1949-04-01 08:00:00    129\n",
       "4   1949-05-01 08:00:00    121\n",
       "..                  ...    ...\n",
       "139 1960-08-01 07:00:00    606\n",
       "140 1960-09-01 07:00:00    508\n",
       "141 1960-10-01 08:00:00    461\n",
       "142 1960-11-01 08:00:00    390\n",
       "143 1960-12-01 08:00:00    432\n",
       "\n",
       "[144 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the TimeSeriesData object with the extra arguments to accomodate unix time \n",
    "ts_from_unixtime = TimeSeriesData(\n",
    "        time=air_passengers_ts_unixtime, \n",
    "        value=air_passengers_df.value, \n",
    "        use_unix_time=True, \n",
    "        unix_time_units=\"s\"\n",
    ")\n",
    "\n",
    "ts_from_unixtime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 `TimeSeriesData` built-in operations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `TimeSeriesData` object supports many of the same operations of the standard `pandas.DataFrame`, including:\n",
    "- Slicing\n",
    "- Math Operations\n",
    "- Extend \n",
    "- Plotting\n",
    "- Utility Functions (`to_dataframe`, `to_array`, `is_empty`, `is_univariate`)\n",
    "\n",
    "We give examples of each as follows."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949-02-01</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949-03-01</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949-04-01</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949-05-01</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        time  value\n",
       "0 1949-02-01    118\n",
       "1 1949-03-01    132\n",
       "2 1949-04-01    129\n",
       "3 1949-05-01    121"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_passengers_ts[1:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Math operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>1949-02-01</td>\n",
       "      <td>236</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949-03-01</td>\n",
       "      <td>264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949-04-01</td>\n",
       "      <td>258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949-05-01</td>\n",
       "      <td>242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        time  value\n",
       "0 1949-02-01    236\n",
       "1 1949-03-01    264\n",
       "2 1949-04-01    258\n",
       "3 1949-05-01    242"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_passengers_ts[1:5] + air_passengers_ts[1:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Equality and Inequality are also supported:\n",
    "\n",
    "air_passengers_ts == air_passengers_ts_from_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_ts == multi_ts_from_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "144"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# length\n",
    "\n",
    "len(air_passengers_ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949-01-01</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949-02-01</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949-03-01</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949-04-01</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949-05-01</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1949-06-01</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1949-07-01</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        time  value\n",
       "0 1949-01-01    112\n",
       "1 1949-02-01    118\n",
       "2 1949-03-01    132\n",
       "3 1949-04-01    129\n",
       "4 1949-05-01    121\n",
       "5 1949-06-01    135\n",
       "6 1949-07-01    148"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating two slices\n",
    "ts_1 = air_passengers_ts[0:3]\n",
    "ts_2 = air_passengers_ts[3:7]\n",
    "\n",
    "ts_1.extend(ts_2)\n",
    "ts_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# Must pass the name of the value columns to plot\n",
    "air_passengers_ts.plot(cols=['value'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can plot multiple time series from multi_ts by passing in the name of each value column we want to plot\n",
    "multi_ts.plot(cols=['v1','v2'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Utility functions\n",
    "\n",
    "Here we provide examples of a few useful Kats utility functions for `TimeSeriesData`.  They can be helpful for working with external libraries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert to `pandas.DataFrame`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949-01-01</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949-02-01</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949-03-01</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949-04-01</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949-05-01</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        time  value\n",
       "0 1949-01-01    112\n",
       "1 1949-02-01    118\n",
       "2 1949-03-01    132\n",
       "3 1949-04-01    129\n",
       "4 1949-05-01    121"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_passengers_ts.to_dataframe().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert to `numpy.ndarray`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[Timestamp('1949-01-01 00:00:00'), 112],\n",
       "       [Timestamp('1949-02-01 00:00:00'), 118],\n",
       "       [Timestamp('1949-03-01 00:00:00'), 132],\n",
       "       [Timestamp('1949-04-01 00:00:00'), 129],\n",
       "       [Timestamp('1949-05-01 00:00:00'), 121]], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_passengers_ts.to_array()[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check basic characteristics of the time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_passengers_ts.is_empty()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_passengers_ts.is_univariate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_ts.is_univariate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Forecasting with Kats\n",
    "\n",
    "We currently support the following 10 base forecasting models: \n",
    "\n",
    "1. Linear  \n",
    "2. Quadratic   \n",
    "3. ARIMA   \n",
    "4. SARIMA   \n",
    "5. Holt-Winters   \n",
    "6. Prophet   \n",
    "7. AR-Net   \n",
    "8. LSTM   \n",
    "9. Theta   \n",
    "10. VAR   \n",
    "\n",
    "\n",
    "Each models follows the `sklearn` model API pattern:  we create an instance of the model class and then call its `fit` and `predict` methods.  In this section, we provide examples for the Prophet and Theta models.  A more in-depth introduction to forecasting in Kats is provided in the Kats 201 tutorial.\n",
    "\n",
    "\n",
    "\n",
    "## 2.1 An example with Prophet model\n",
    "\n",
    "We will demonstrate how to use Prophet model to forecast with the `air_passengers` data set. Note: this example requires that fbprophet be installed (for example, `pip install kats[prophet]` or `pip install kats[all]`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial log joint probability = -2.46502\n",
      "    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes \n",
      "      99       501.164    0.00404329       133.096      0.7795      0.7795      135   \n",
      "    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes \n",
      "     199       503.029    0.00011625       61.0812      0.7463      0.7463      258   \n",
      "    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes \n",
      "     299       503.241   0.000197464       74.1894      0.6815      0.6815      384   \n",
      "    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes \n",
      "     376       503.429   0.000269211       170.518   3.444e-06       0.001      512  LS failed, Hessian reset \n",
      "     399       503.521    0.00012352       74.8921      0.4361      0.4361      542   \n",
      "    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes \n",
      "     428       503.524   4.87946e-06       79.4582   6.436e-08       0.001      619  LS failed, Hessian reset \n",
      "     456       503.524   8.65799e-08       75.9689      0.9655      0.9655      656   \n",
      "Optimization terminated normally: \n",
      "  Convergence detected: relative change in objective function was below tolerance\n"
     ]
    }
   ],
   "source": [
    "# import the param and model classes for Prophet model\n",
    "from kats.models.prophet import ProphetModel, ProphetParams\n",
    "\n",
    "# create a model param instance\n",
    "params = ProphetParams(seasonality_mode='multiplicative') # additive mode gives worse results\n",
    "\n",
    "# create a prophet model instance\n",
    "m = ProphetModel(air_passengers_ts, params)\n",
    "\n",
    "# fit model simply by calling m.fit()\n",
    "m.fit()\n",
    "\n",
    "# make prediction for next 30 month\n",
    "fcst = m.predict(steps=30, freq=\"MS\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>fcst</th>\n",
       "      <th>fcst_lower</th>\n",
       "      <th>fcst_upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1961-01-01</td>\n",
       "      <td>451.152463</td>\n",
       "      <td>437.076468</td>\n",
       "      <td>463.801595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1961-02-01</td>\n",
       "      <td>432.553525</td>\n",
       "      <td>419.579225</td>\n",
       "      <td>446.078957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1961-03-01</td>\n",
       "      <td>491.921076</td>\n",
       "      <td>478.944870</td>\n",
       "      <td>505.818651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1961-04-01</td>\n",
       "      <td>494.501049</td>\n",
       "      <td>481.120020</td>\n",
       "      <td>508.717441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1961-05-01</td>\n",
       "      <td>503.407884</td>\n",
       "      <td>490.099399</td>\n",
       "      <td>517.321676</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        time        fcst  fcst_lower  fcst_upper\n",
       "0 1961-01-01  451.152463  437.076468  463.801595\n",
       "1 1961-02-01  432.553525  419.579225  446.078957\n",
       "2 1961-03-01  491.921076  478.944870  505.818651\n",
       "3 1961-04-01  494.501049  481.120020  508.717441\n",
       "4 1961-05-01  503.407884  490.099399  517.321676"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the predict method returns a dataframe as follows\n",
    "fcst.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='time', ylabel='y'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the results with uncertainty intervals\n",
    "m.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 An example with Theta model\n",
    "\n",
    "\n",
    "We will now use the Theta model to forecast with the `air_passengers` data set.  \n",
    "\n",
    "The Theta Method (Assimakopoulos and Nikolopoulos, 2000) is a univariate forecasting method that fits two Theta lines: 1) a linear interpolation (called the `Theta=0`) and 2) a second-order difference (called the `Theta=2` line), and then combines them to build a forecast.  Prior to running this forecast, we test the time series for seasonality, deseaonalize if seasonality is detected, and then reseasonalize the calculated forecasts.  \n",
    "\n",
    "Hyndman and Billah (2003) showed that the Theta Method is equivalent to simple exponential smoothing with drift.  In Kats we use this underlying model to calculate prediction intervals for `ThetaModel`.\n",
    "\n",
    "Our implementation of `ThetaModel` in Kats is similar to the [thetaf function in R](https://pkg.robjhyndman.com/forecast/reference/thetaf.html).  Because each of our time series models follow the `sklearn` model API pattern, the code using `ThetaModel` is quite similar to the example above using \n",
    "`ProphetModel`: we initialize the model with its parameters and then call the `fit` and `predict` methods.  We can then use the `plot` method to visualize our forecast."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='time', ylabel='y'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import param and model from `kats.models.theta`\n",
    "from kats.models.theta import ThetaModel, ThetaParams\n",
    "\n",
    "# create ThetaParam with specifying seasonality param value\n",
    "params = ThetaParams(m=12)\n",
    "\n",
    "# create ThetaModel with given data and parameter class\n",
    "m = ThetaModel(data=air_passengers_ts, params=params)\n",
    "\n",
    "# call fit method to fit model\n",
    "m.fit()\n",
    "\n",
    "# call predict method to predict the next 30 steps\n",
    "res = m.predict(steps=30, alpha=0.2)\n",
    "\n",
    "# visualize the results\n",
    "m.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Detection with Kats\n",
    "\n",
    "Kats provides a set of models and algorithms to detect outliers, change points, and trend changes in time series data.\n",
    "\n",
    "\n",
    "## 3.1 What are the algorithms?\n",
    "\n",
    "To detect a specific pattern, we provided different algorithms, which is summarized as follows.\n",
    "- **Outlier Detection**. This usually refers to a abnormal spike in a time series data, which can be detected with `OutlierDetector`\n",
    "- **Change Point Detection**. This refers to a sudden change that the time series have different statistical properties before and after the change. We provided three major algorithms to detect such patterns:\n",
    "    - CUSUM Detection\n",
    "    - Bayesian Online Change Point Detection (BOCPD)\n",
    "    - Stat Sig Detection\n",
    "- **Trend Change Detection**. This refers to a slow trend change on the time series data, which can be detected with Mann-Kendall detection algorithm, `MKDetector`\n",
    "\n",
    "In this tutorial, we will demonstrate the usage of two detectors: `OutlierDetector` and `CUSUM`.  A more in-depth introduction to detection in Kats is provided in the Kats 202 tutorial.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 An example with outlier detection method\n",
    "\n",
    "We provide the `OutlierDetector` module to detect outliers in time series.  Since outliers can cause so many problems in downstream processing, it is important to be able to detect them.  `OutlierDetector` also provides functionality to handle or remove outliers once they are found.\n",
    "\n",
    "Our outlier detection algorithm works as follows:\n",
    "\n",
    "- We do a [seasonal decomposition](https://www.statsmodels.org/stable/generated/statsmodels.tsa.seasonal.seasonal_decompose.html) of the input time series with additive or multiplicative decomposition as specified (default is additive)\n",
    "- We generate a residual time series by either removing only trend or both trend and seasonality if the seasonality is strong.\n",
    "- We detect points in the residual which are outside 3 times the inter quartile range.  This multiplier can be tuned using the `iqr_mult` parameter in `OutlierDetector`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our example below copies the `air_passengers` data set and manually inserts outliers into it. We then use `OutlierDetector` to find them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# deep copy the air_passenger_df \n",
    "air_passengers_outlier_df = air_passengers_df.copy(deep=True)\n",
    "\n",
    "# manually add outlier on the date of '1950-12-01'\n",
    "air_passengers_outlier_df.loc[air_passengers_outlier_df.time == '1950-12-01','value']*=5\n",
    "# manually add outlier on the date of '1959-12-01'\n",
    "air_passengers_outlier_df.loc[air_passengers_outlier_df.time == '1959-12-01', 'value']*=4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the raw data\n",
    "air_passengers_outlier_df.plot(x='time', y='value', figsize=(15,8))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform the outlier data into `TimeSeriesData` Object\n",
    "air_passengers_outlier_ts = TimeSeriesData(air_passengers_outlier_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from kats.detectors.outlier import OutlierDetector\n",
    "\n",
    "ts_outlierDetection = OutlierDetector(air_passengers_outlier_ts, 'additive') # call OutlierDetector\n",
    "ts_outlierDetection.detector() # apply OutlierDetector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we look at the outliers that the algorithm found."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Timestamp('1950-12-01 00:00:00'),\n",
       " Timestamp('1959-11-01 00:00:00'),\n",
       " Timestamp('1959-12-01 00:00:00'),\n",
       " Timestamp('1960-11-01 00:00:00'),\n",
       " Timestamp('1960-12-01 00:00:00')]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_outlierDetection.outliers[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After detecting the outlier, we can now easily removal them from the data. Here we will explore two options: \n",
    "- **No Interpolation**: outlier data points will be replaced with **NaN** values\n",
    "- **With Interpolation**: outlier data points will be replaced with **linear interploation** values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "air_passengers_ts_outliers_removed = ts_outlierDetection.remover(interpolate = False) # No interpolation\n",
    "air_passengers_ts_outliers_interpolated = ts_outlierDetection.remover(interpolate = True) # With interpolation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we visualize the difference between these two approaches to removing outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20,8), nrows=1, ncols=2)\n",
    "\n",
    "air_passengers_ts_outliers_removed.to_dataframe().plot(x = 'time',y = 'y_0', ax= ax[0])\n",
    "ax[0].set_title(\"Outliers Removed : No interpolation\")\n",
    "air_passengers_ts_outliers_interpolated.to_dataframe().plot(x = 'time',y = 'y_0', ax= ax[1])\n",
    "ax[1].set_title(\"Outliers Removed : With interpolation\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3 An example with CUSUM algorithm\n",
    "\n",
    "Cusum is a method to detect an up/down shift of means in a time series. Our implementation has two main steps:\n",
    "\n",
    "1.  **Locate the change point:** This is an iterative process where we initialize a change point (in the middle of the time series) and CUSUM time series based on this change point.  The next changepoint is the location where the previous CUSUM time series is maximized (or minimized).  This iteration continues until either 1) a stable changepoint is found or 2) we exceed the limit number of iterations.\n",
    "\n",
    "2.  **Test the change point for statistical significance:** Conduct log likelihood ratio test to test if the mean of the time series changes at the changepoint calculated in Step 1.  The null hypothesis is that there is no change in mean.\n",
    "\n",
    "By default, we report a detected changepoint if and only if we reject the null hypothesis in Step 2.  \n",
    "\n",
    "Here are a few additional points worth mentioning:\n",
    "- We assume there is at most one increase change point and at most one decrease change point.  You can use the `change_directions` argument in the detector to specify whether you are looking an increase, a decrease, or both (default is both).\n",
    "- We use Gaussian distribution as the underlying model to calculate the CUSUM time series value and conduct the hypothesis test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import packages\n",
    "from kats.consts import TimeSeriesData, TimeSeriesIterator\n",
    "from kats.detectors.cusum_detection import CUSUMDetector\n",
    "\n",
    "# synthesize data with simulation\n",
    "np.random.seed(10)\n",
    "df = pd.DataFrame(\n",
    "    {\n",
    "        'time': pd.date_range('2019-01-01', '2019-03-01'),\n",
    "        'increase':np.concatenate([np.random.normal(1,0.2,30), np.random.normal(2,0.2,30)]),\n",
    "        'decrease':np.concatenate([np.random.normal(1,0.3,50), np.random.normal(0.5,0.3,10)]),\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is straightforward to use `CUSUMDetector` to detect an increase or a decrease when there is one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# detect increase\n",
    "timeseries = TimeSeriesData(\n",
    "    df.loc[:,['time','increase']]\n",
    ")\n",
    "detector = CUSUMDetector(timeseries)\n",
    "\n",
    "# run detector\n",
    "change_points = detector.detector(change_directions=[\"increase\"])\n",
    "\n",
    "# plot the results\n",
    "detector.plot(change_points)\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# detect decrease\n",
    "timeseries = TimeSeriesData(\n",
    "    df.loc[:,['time','decrease']]\n",
    ")\n",
    "detector = CUSUMDetector(timeseries)\n",
    "\n",
    "# run detector\n",
    "change_points = detector.detector(change_directions=[\"decrease\"])\n",
    "\n",
    "# plot the results\n",
    "detector.plot(change_points)\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we try to detect a decrease in a series where there is only an increase, no change point will be found."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:No change points detected!\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# detect increase\n",
    "timeseries = TimeSeriesData(\n",
    "    df.loc[:,['time','increase']]\n",
    ")\n",
    "detector = CUSUMDetector(timeseries)\n",
    "\n",
    "# run detector\n",
    "change_points = detector.detector(change_directions=[\"decrease\"])\n",
    "\n",
    "# plot the results\n",
    "detector.plot(change_points)\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we do not specify which change directions we are looking for, `CUSUMDetector` will look for both increases and decreases.  In the case below where there is only an increase, it will detect that increase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# detect increase\n",
    "timeseries = TimeSeriesData(\n",
    "    df.loc[:,['time','increase']]\n",
    ")\n",
    "detector = CUSUMDetector(timeseries)\n",
    "\n",
    "# run detector\n",
    "change_points = detector.detector()\n",
    "\n",
    "# plot the results\n",
    "detector.plot(change_points)\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Feature Extraction with Kats\n",
    "\n",
    "We provide the `TsFeatures` module to calculate a set of meaningful features for a time series, including:\n",
    "- STL (Seasonal and Trend decomposition using Loess) Features\n",
    "    - Strength of Seasonality\n",
    "    - Strength of Trend\n",
    "    - Spikiness\n",
    "    - Linearity\n",
    "- Amount of Level Shift\n",
    "- Presence of Flat Segments\n",
    "- ACF and PACF Features\n",
    "- Hurst Exponent\n",
    "- ARCH Statistic\n",
    "\n",
    "Given a collection of time series, these features can be used to identify specific series that are similar or outlying.  Our `TsFeatures` module is similar to the one that is [freely available in R](https://pkg.robjhyndman.com/tsfeatures/index.html).\n",
    "\n",
    "These features also play a crucial role in many downstream projects, including \n",
    "1. “Meta-learning”, i.e., choosing the best forecasting model based on characteristics of the input time series \n",
    "2. Time series classification and clustering analysis\n",
    "3. Nowcasting algorithms for better short-term forecasting\n",
    "\n",
    "Now we will show you how to use `TsFeatures` get the features for the `air_passenger` data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initiate feature extraction class\n",
    "from kats.tsfeatures.tsfeatures import TsFeatures\n",
    "tsFeatures = TsFeatures()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_air_passengers = TsFeatures().transform(air_passengers_ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can see the dictionary of features as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'length': 144,\n",
       " 'mean': 280.2986111111111,\n",
       " 'var': 14291.97333140432,\n",
       " 'entropy': 0.4287365561752448,\n",
       " 'lumpiness': 3041164.5629058965,\n",
       " 'stability': 12303.627266589507,\n",
       " 'flat_spots': 2,\n",
       " 'hurst': -0.0802329103051348,\n",
       " 'std1st_der': 27.206287853461966,\n",
       " 'crossing_points': 7,\n",
       " 'binarize_mean': 0.4444444444444444,\n",
       " 'unitroot_kpss': 0.12847508180149234,\n",
       " 'heterogeneity': 126.0645062581934,\n",
       " 'histogram_mode': 155.8,\n",
       " 'linearity': 0.853638165603188,\n",
       " 'trend_strength': 0.9383301875692747,\n",
       " 'seasonality_strength': 0.3299338017939569,\n",
       " 'spikiness': 111.69732482853489,\n",
       " 'peak': 6,\n",
       " 'trough': 3,\n",
       " 'level_shift_idx': 118,\n",
       " 'level_shift_size': 15.599999999999966,\n",
       " 'y_acf1': 0.9480473407524915,\n",
       " 'y_acf5': 3.392072131604336,\n",
       " 'diff1y_acf1': 0.30285525815216935,\n",
       " 'diff1y_acf5': 0.2594591065999471,\n",
       " 'diff2y_acf1': -0.19100586757092733,\n",
       " 'diff2y_acf5': 0.13420736423784568,\n",
       " 'y_pacf5': 1.003288249401529,\n",
       " 'diff1y_pacf5': 0.2194123478008142,\n",
       " 'diff2y_pacf5': 0.2610103428699484,\n",
       " 'seas_acf1': 0.6629043863684492,\n",
       " 'seas_pacf1': 0.15616955255589107,\n",
       " 'firstmin_ac': 8,\n",
       " 'firstzero_ac': 52,\n",
       " 'holt_alpha': 0.995,\n",
       " 'holt_beta': 0.0001,\n",
       " 'hw_alpha': 0.9999999850988388,\n",
       " 'hw_beta': 4.337812820497186e-15,\n",
       " 'hw_gamma': 1.3205905395611146e-08}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# features_air_passengers\n",
    "features_air_passengers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Summary\n",
    "\n",
    "in this tutorial, we have shown the basic operations of **Kats** in time series application, including basic introductions to forecasting, detection, feature extraction in Kats.\n",
    "\n",
    "If you want to explore these topics in more detail, you can check out Kats 20x tutorials:\n",
    "\n",
    "- [Kats 201 Forecasting with Kats](kats_201_forecasting.ipynb)\n",
    "- [Kats 202 Detection with Kats](kats_202_detection.ipynb)\n",
    "- [Kats 203 Time Series Features](kats_203_tsfeatures.ipynb)\n",
    "- [Kats 204 Forecasting with Meta-Learning](kats_204_metalearning.ipynb)\n",
    "- [Kats 205 Forecasting with Global Model](kats_205_globalmodel.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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